Fast two-stage spectrum detector for cognitive radios in uncertain noise channels

Nair, Prashob R.; Vinod, A. P.; Smitha, K. G.; Krishna, Anoop Kumar

doi:10.1049/iet-com.2011.0751

Cited by 23 publications

(23 citation statements)

References 17 publications

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“…In [31] channels are examined sequentially, and every channel whose energy in the coarse stage does not exceed a pre-defined threshold is immediately examined in detail using a cyclostationary feature detection sensing technique. In a similar manner, in [32] energy detection and cyclostationary feature detection are applied in the coarse and fine sensing stages, respectively, but the latter is omitted when the decision at the former is sufficient to characterize the channel's occupancy with significant confidence.…”

Section: Coarse-fine Sensing Approachesmentioning

confidence: 99%

Discovering and exploiting spectrum power correlations in cognitive radio networks: an experimentally driven approach

Arkoulis

Anifantis

Karyotis

et al. 2014

J Wireless Com Network

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In this paper, we focus on increasing the spectrum awareness of cognitive radio users through statistical processing of spectrum sensing data, obtained via wideband energy-detection-based sensing techniques. Based on observations over real spectrum power measurements, we advocate the existence of correlation properties in the sensed power of measured neighboring channels and propose an inference methodology for exploiting them towards acquiring a more accurate view of the underlying wireless environment. To highlight the benefits of the proposed correlation-based inference methodology on cognitive radio systems, we emphasize on enhancements of white space discovery and channel selection processes, while we thoroughly discuss the impact of our findings on existing relevant approaches. Based on a systematic wireless spectrum survey in the metropolitan area of Athens, Greece, we validate our proposed methodology and assess the achieved performance improvements through the obtained measurement data, confirming its potential value in future cognitive radio networks.

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Section: Coarse-fine Sensing Approachesmentioning

confidence: 99%

Discovering and exploiting spectrum power correlations in cognitive radio networks: an experimentally driven approach

Arkoulis

Anifantis

Karyotis

et al. 2014

J Wireless Com Network

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“…An improved version of [9] in terms of mean detection time is proposed in [10]. It achieves the same probability of detection and false alarm with much less mean detection time.…”

Section: Related Workmentioning

confidence: 99%

“…This approach can be very healthy for CR applications for disaster management; smart grid, and so on to get reliable sensing results with minimum sensing time. Many improved local sensing schemes are proposed in [5][6][7][8][9][10][11][12], including our own fuzzy logic-based and SNR-based adaptive spectrum sensing for improved local sensing. In the proposed scheme, channels with known PU waveform will be sensed by matched filter detection and rest of the channels by the detectors which do not need dedicated circuitry and prior knowledge of PU waveform.…”

Section: Introductionmentioning

confidence: 99%

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

Ejaz

Hasan

Lee

et al. 2013

J Wireless Com Network

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Reliable spectrum sensing is one of the most crucial aspects for the successful deployment of cognitive radio (CR) technology. For CR, it is not possible to transmit on a licensed band and sense it simultaneously, therefore sensing must be interleaved with transmission. Spectrum sensing in CR is challenged by a number of uncertainties, which degrade the sensing performance and in turn require much more time to achieve the targeted sensing efficiency. Hence, the authors propose a spectrum sensing scheme which obtains reliable results with less mean detection time. First, the scheme determines a better matched filter, or a combination of energy and cyclostationary detectors based on the power and band of interest. In the combined energy and cyclostationary detector, an energy detector with a bi-threshold is used, and the cyclostationary detector is applied only if the energy of the signal lies between two thresholds. Second, sensing is performed by the selection choice resulting from the first step. To evaluate the scheme's performance, the results are compared with those where only an energy detector, matched filter, or cyclostationary detector are performed. The performance metrics are the probability of detection, probability of false alarm, and mean detection time.

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“…energy detection, cyclostationary feature detector (CFD) etc., detect whether the band is vacant or not. A two-stage spectrum sensing has been proposed in [94,95,105,106] which consists of basic sensing stage followed by advanced sensing stage. The main function of the basic sensing stage is to detect strong primary as well as secondary users quickly and the energy detection is generally preferred choice for the basic sensing stage [94,95].…”

Section: Area and Power Efficient Two-stage Spectrum Sensingmentioning

confidence: 99%

“…However, the area complexity and power consumption of two-stage spectrum sensing is very high and dominated by CFDs. The spectrum sensing schemes in [105,106] provide solutions to significantly reduce the dynamic power consumption of two-stage spectrum sensing up to 84% when the SNR is high but area complexity and static power consumption remains the same.…”

Section: Area and Power Efficient Two-stage Spectrum Sensingmentioning

confidence: 99%

Design of low complexity variable digital filters and reconfigurable filter banks for multi-standard wireless communication receivers

Darak¹

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Fast two-stage spectrum detector for cognitive radios in uncertain noise channels

Cited by 23 publications

References 17 publications

Discovering and exploiting spectrum power correlations in cognitive radio networks: an experimentally driven approach

Discovering and exploiting spectrum power correlations in cognitive radio networks: an experimentally driven approach

I3S: Intelligent spectrum sensing scheme for cognitive radio networks

Design of low complexity variable digital filters and reconfigurable filter banks for multi-standard wireless communication receivers

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